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B 型超声图像中肌肉束方向的自动跟踪

Automated tracking of muscle fascicle orientation in B-mode ultrasound images.

作者信息

Rana Manku, Hamarneh Ghassan, Wakeling James M

机构信息

School of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada.

出版信息

J Biomech. 2009 Sep 18;42(13):2068-73. doi: 10.1016/j.jbiomech.2009.06.003. Epub 2009 Jul 30.

DOI:10.1016/j.jbiomech.2009.06.003
PMID:19646699
Abstract

B-mode ultrasound can be used to non-invasively image muscle fascicles during both static and dynamic contractions. Digitizing these muscle fascicles can be a timely and subjective process, and usually studies have used the images to determine the linear fascicle lengths. However, fascicle orientations can vary along each fascicle (curvature) and between fascicles. The purpose of this study was to develop and test two methods for automatically tracking fascicle orientation. Images were initially filtered using a multiscale vessel enhancement (a technique used to enhance tube-like structures), and then fascicle orientations quantified using either the Radon transform or wavelet analysis. Tests on synthetic images showed that these methods could identify fascicular orientation with errors of less than 0.06 degrees . Manual digitization of muscle fascicles during a dynamic contraction resulted in a standard deviation of angle estimates of 1.41 degrees across ten researchers. The Radon transform predicted fascicle orientations that were not significantly different from the manually digitized values, whilst the wavelet analysis resulted in angles that were 1.35 degrees less, and reasons for these differences are discussed. The Radon transform can be used to identify the dominant fascicular orientation within an image, and thus used to estimate muscle fascicle lengths. The wavelet analysis additionally provides information on the local fascicle orientations and can be used to quantify fascicle curvatures and regional differences with fascicle orientation across an image.

摘要

B 型超声可用于在静态和动态收缩过程中对肌肉束进行无创成像。将这些肌肉束数字化可能是一个耗时且主观的过程,并且通常研究使用图像来确定肌肉束的线性长度。然而,肌肉束的方向可能沿每个肌肉束(曲率)以及在不同肌肉束之间发生变化。本研究的目的是开发并测试两种自动跟踪肌肉束方向的方法。图像首先使用多尺度血管增强技术(一种用于增强管状结构的技术)进行滤波,然后使用拉东变换或小波分析对肌肉束方向进行量化。对合成图像的测试表明,这些方法能够识别肌肉束方向,误差小于 0.06 度。在动态收缩过程中对肌肉束进行手动数字化时,十位研究人员角度估计的标准差为 1.41 度。拉东变换预测的肌肉束方向与手动数字化的值无显著差异,而小波分析得出的角度小 1.35 度,并对这些差异的原因进行了讨论。拉东变换可用于识别图像内主要的肌肉束方向,从而用于估计肌肉束长度。小波分析还提供有关局部肌肉束方向的信息,可用于量化肌肉束曲率以及图像中肌肉束方向的区域差异。

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